Programming Research Articles

Short Cyclic Algorithm for Turbo Block Construction

This paper presents perhaps the shortest possible algorithm for drafting a turbo block in a standard CAD environment. Though not defining the complete turbo roundabout geometry (the legs are defined by their centerlines only), turbo block construction stays at the very core of its geometrical definition. Relevant national policies elaborate turbo block construction at various levels of detail. Some policies skip certain definitions of spirals’ centers and some even allow for slight discontinuities in spiraling lanes’ geometries. But, as our computer program had to be based on precisely defined geometrical features, no question, be it a location of a particular center or relation between the interconnecting arcs, could be left unanswered. Consequently, while explaining the basics on which the program relies, all the elements of the turbo block are discussed. Even the problems not addressed in renowned national policies (such as alternatively matching marking and curb lines) had to be resolved before being incorporated into the program. After resolving and elaborating turbo block roundabout technics in detail, a new cyclic algorithm for turbo block construction is presented. The program is very short and could be easily understood and modified by the average programmer.

Determination of Load Forces on the Upper Part of Orthopedic and Postoperative Shoes

Determined patterns of force load on the sole and upper of orthopedic and postoperative shoes when bending the foot while walking. To determine the force of the load on the top of the postoperative orthopedic shoes, the frictional forces between the lateral surfaces of the shoes and the outer lateral sides of the foot can be neglected. Thus, the task will be reduced to the compilation of equilibrium equations of a plane system of forces. As can be seen from the load diagram of the top of the shoe, we have a flat arbitrarily located system of forces. To determine the unknown forces, it is necessary to compile a system of two equilibrium equations, which will represent the algebraic sums of the projections of all forces on the horizontal and vertical axes. To automate the design process of orthopedic and postoperative shoes, a computer program was developed that allows you to perform calculations of the corresponding forces and tension depending on the design and types of material of individual elements, as well as obtain regression equations for determining the effect of tension on the magnitude of the detachment force.

Analysing the Capital Structure of Indian Entertainment and Pharmaceutical Industry: A Comparative Study

This research work has been done through the various analysis regarding the capital structure of different industries. The work has been done to know the different capital structure mix of Entertainment and Pharmaceutical industry of India. Where to know the Capital structure impact on the profitability of different companies along with Covid 19 period. I also had been used the statistical analysis for comparison of both the industries which is R Software programme. The panel data models had been analysed in this research with Fixed effect method of Least squared Dummy Variable [LSDV] model.

Enthalpy increment of PbBi12O19(s) and ϕ-Pb5Bi8O17(s) by drop calorimetry

Enthalpy increments of PbBi12O19(s) and ϕ-Pb5Bi8O17(s) were measured by drop calorimetry in the temperature range 463–895 K and 475–757 K, respectively. A Python-based computer program implementing the Shomate method was coded to fit the experimental data. The expressions for enthalpy increment of PbBi12O19(s) and ϕ-Pb5Bi8O17(s) as a function of temperature are:HT0-H2980 < PbBi12O19> (J/mol) = 180.994×T+4939.217×10-4×T2-1746.59×104×T-1-39289 (T: 463 – 895 K)HT0-H2980 < ϕ-Pb5Bi8O17> (J/mol) = 792.256×T-65.626×10-4×T2+1638.68×104×T-1-290589 (T: 475 – 757 K)Entropies and Gibbs energy functions were also derived from the enthalpy increment data.

Decision-Making Method for the Evaluation of Success Factors of ABET Accreditation

ABET (Accreditation Board for Engineering and Technology), formerly known as the Accreditation Board for Engineering and Technology, accredits university programs in computers, engineering, and engineering technology. Although ABET accreditation is optional, graduates of programs with this accreditation are regarded as having knowledge, attitudes, and behaviours that are comparable to those of other countries. The ABET Accreditation process has always been quite structured and methodical. It has helped identify and fix any inadequacies by comparing different engineering and computer programs to worldwide standards. The Decision Making Trial and Evaluation Laboratory (DEMATEL) approach was used in this study to ascertain the relative value of the ABET process’s success elements. An application case study, conducted at a Turkish institution, serves as an illustration.

Inversion of downhole resistivity properties through infrared spectroscopy and whole‐rock geochemistry using machine‐learning

AbstractThe electrical properties of rocks are widely used in the geophysical exploration of natural resources, such as minerals, hydrocarbons and groundwater. In mining exploration, the primary goal is to map electrically anomalous geological features associated with different mineralization styles, such as clay alteration haloes, metal oxides and sulphides, weathered crystalline rocks or fractured zones. As such, the reconciliation of geophysical data with geological information (geochemistry, mineralogy, texture and lithology) is a critical step and can be performed based on petrophysical properties collected either on core samples or as downhole measurements. Based on data from 189 diamond drill cores collected for uranium exploration in the Athabasca Basin (Saskatchewan, Canada), this paper presents a case study of reconciliation of downhole resistivity probing with core sample geochemistry and short‐wave infrared spectroscopy (350–2500 nm) through three successive steps: (i) multivariate analysis of resistivity and other petrophysical properties (porosity, density) against geochemical and infrared spectroscopy information to characterize electrical properties of rocks with respect to other physical parameters, (ii) a machine‐learning workflow integrating geochemistry and spectral signatures in order to infer synthetic resistivity logs along with uncertainties. The best model in the basin was Light Gradient‐Boosting Machine with pairwise log‐ratio, which yielded a coefficient of determination R2 = 0.80 (root mean square error = 0.16), and in the basement, support vector regression with data fusion of infrared spectroscopy and pairwise log‐ratios on geochemistry yielded R2 = 0.82 (root mean square error = 0.35); (iii) the best model was then fitted on an area that was excluded from the original dataset (Getty Russell property) in order to infer synthetic resistivity logs for that zone. Software code is publicly available. This workflow can be re‐used for the valorization of legacy datasets.

Applying Spatiotemporal Modeling of Cell Dynamics to Accelerate Drug Development.

Cells act as physical computational programs that utilize input signals to orchestrate molecule-level protein-protein interactions (PPIs), generating and responding to forces, ultimately shaping all of the physiological and pathophysiological behaviors. Genome editing and molecule drugs targeting PPIs hold great promise for the treatments of diseases. Linking genes and molecular drugs with protein-performed cellular behaviors is a key yet challenging issue due to the wide range of spatial and temporal scales involved. Building predictive spatiotemporal modeling systems that can describe the dynamic behaviors of cells intervened by genome editing and molecular drugs at the intersection of biology, chemistry, physics, and computer science will greatly accelerate pharmaceutical advances. Here, we review the mechanical roles of cytoskeletal proteins in orchestrating cellular behaviors alongside significant advancements in biophysical modeling while also addressing the limitations in these models. Then, by integrating generative artificial intelligence (AI) with spatiotemporal multiscale biophysical modeling, we propose a computational pipeline for developing virtual cells, which can simulate and evaluate the therapeutic effects of drugs and genome editing technologies on various cell dynamic behaviors and could have broad biomedical applications. Such virtual cell modeling systems might revolutionize modern biomedical engineering by moving most of the painstaking wet-laboratory effort to computer simulations, substantially saving time and alleviating the financial burden for pharmaceutical industries.

Extracting efficient exact real number computation from proofs in constructive type theory

Abstract Exact real computation is an alternative to floating-point arithmetic where operations on real numbers are performed exactly, without the introduction of rounding errors. When proving the correctness of an implementation, one can focus solely on the mathematical properties of the problem without thinking about the subtleties of representing real numbers. We propose a new axiomatization of the real numbers in a dependent type theory with the goal of extracting certified exact real computation programs from constructive proofs. Our formalization differs from similar approaches, in that we formalize the reals in a conceptually similar way as some mature implementations of exact real computation. Primitive operations on reals can be extracted directly to the corresponding operations in such an implementation, producing more efficient programs. We particularly focus on the formalization of partial and nondeterministic computation, which is essential in exact real computation. We prove the soundness of our formalization with regards to the standard realizability interpretation from computable analysis and show how to relate our theory to a classical formalization of the reals. We demonstrate the feasibility of our theory by implementing it in the Coq proof assistant and present several natural examples. From the examples we have automatically extracted Haskell programs that use the exact real computation framework AERN for efficiently performing exact operations on real numbers. In experiments, the extracted programs behave similarly to native implementations in AERN in terms of running time and memory usage.

Comparative evaluation of commercially available AI-based cephalometric tracing programs

ObjectivesCompare the accuracy and diagnostic concordance of three commercially available AI-based lateral cephalometric tracing software.Materials and methodsSixty-three lateral cephalometric radiographs were analyzed using semi-automatic (Dolphin Imaging Systems LLC) and AI-based software programs (WebCeph™, Cephio, and Ceppro DDH Inc.). Intra- and inter-observer reliability were assessed for human expert measurements, and repeated-measures one-way ANOVA was used to compare the AI and human expert measurements. The diagnostic performance was evaluated using sensitivity and specificity tests.ResultsHuman expert reliability was excellent (ICC > 0.9) for most cephalometric parameters. Compared to human experts, significant differences were observed for all three AI-based cephalometric programs (WebCeph™ – 10 of 11, Cephio – 7 of 11, and Ceppro DDH Inc. – 7 of 11 cephalometric measurements). Variations exceeding two units were noted for most parameters, and differences in defining the sagittal and vertical skeletal patterns, dental, and soft tissue characteristics were observed.ConclusionAll three AI-based tracing programs showed inaccuracies compared to human expert measurements and lacked reliability in measuring key cephalometric parameters. Clinicians should exercise caution when relying solely on AI-based analyses for orthodontic treatment planning and assessment.

Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines

Computer numerical control (CNC) and machine center (MCT) machines are mechanical devices that manipulate different tools using computer programming as inputs. Predicting failures in CNC and MCT machines before their actual failure time is crucial to reduce maintenance costs and increase productivity. This study is centered around a novel deep learning-based model using a 1D convolutional neural network (CNN) for early fault detection in MCT machines. We collected sensor-based data from CNC/MCT machines and applied various preprocessing techniques to prepare the dataset. Our experimental results demonstrate that the 1D-CNN model achieves a higher accuracy of 91.57% compared to traditional machine learning classifiers and other deep learning models, including Random Forest (RF) at 89.71%, multi-layer perceptron (MLP) at 87.45%, XGBoost at 89.67%, logistic regression (LR) at 75.93%, support vector machine (SVM) at 75.96%, K-nearest neighbors (KNN) at 82.93%, decision tree at 88.36%, naïve Bayes at 68.31%, long short-term memory (LSTM) at 90.80%, and a hybrid 1D CNN + LSTM model at 88.51%. Moreover, our proposed 1D CNN model outperformed all other mentioned models in precision, recall, and F-1 scores, with 91.87%, 91.57%, and 91.63%, respectively. These findings highlight the efficacy of the 1D CNN model in providing optimal performance with an MCT machine’s dataset, making it particularly suitable for small manufacturing companies seeking to automate early fault detection and classification in CNC and MCT machines. This approach enhances productivity and aids in proactive maintenance and safety measures, demonstrating its potential to revolutionize the manufacturing industry.

The Effect of Cervical Cancer Education Given to Women in Turkey on Knowledge, Attitudes, and Health Beliefs: A Randomized Controlled Study.

The objective of this study was to ascertain the impact of cervical cancer education provided to women in Turkey on their knowledge, attitudes, and health beliefs. The study was conducted in accordance with the pretest-posttest randomized controlled trial model. The participants were randomly assigned to either the experimental or the control groups. The study was completed with a total of 105 women, 51 of whom were in the experimental group and 54 of whom were in the control group. The personal information form, the human papillomavirus (HPV) knowledge scale, and the health belief model scale for cervical cancer and Pap smear test were employed as data collection instruments. The data were analyzed using the SPSS 25.0 package program, and the number, percentage, independent samples t-test, and paired t-test were utilized for data analysis. Upon completion of the training program, the mean scores of the experimental group demonstrated a statistically significant increase in knowledge related to general HPV information, HPV screening tests, general HPV vaccines, and HPV vaccine programs when compared to the control group (p<0.001). Upon conclusion of the educational intervention, it was determined that the mean scores of cervical cancer sensitivity, caring, health motivation, Pap smear utility, and motivation of women in the experimental group exhibited an increase, while their general perception of Pap smear demonstrated a decrease (p<0.05). The findings of this study indicate that educational initiatives targeting women regarding cervical cancer have a beneficial impact on their knowledge, attitudes, and health beliefs. NCT05990270 registered on https://clinicaltrials.gov/.

Deep learning-based enhancement of fluorescence labeling for accurate cell lineage tracing during embryogenesis.

Automated cell lineage tracing throughout embryogenesis plays a key role in the study of regulatory control of cell fate differentiation, morphogenesis and organogenesis in the development of animals, including nematode Caenorhabditis elegans. However, automated cell lineage tracing suffers from an exponential increase in errors at late embryo because of the dense distribution of cells, relatively low signal-to-noise ratio (SNR) and imbalanced intensity profiles of fluorescence images, which demands a huge amount of human effort to manually correct the errors. The existing image enhancement methods are not sensitive enough to deal with the challenges posed by the crowdedness and low signal-to-noise ratio. An alternative method is urgently needed to assist the existing detection methods in improving their detection and tracing accuracy, thereby reducing the huge burden for manual curation. We developed a new method, termed as DELICATE, that dramatically improves the accuracy of automated cell lineage tracing especially during the stage post 350 cells of C. elegans embryo. DELICATE works by increasing the local SNR and improving the evenness of nuclei fluorescence intensity across cells especially in the late embryos. The method both dramatically reduces the segmentation errors by StarryNite and the time required for manually correcting tracing errors up to 550-cell stage, allowing the generation of accurate cell lineage at large-scale with a user-friendly software/interface. All images and data are available at https://doi.org/10.6084/m9.figshare.26778475.v1. The code and user-friendly software are available at https://github.com/plcx/NucApp-develop.

A primer of community ecology using the R language

AbstractCommunity ecology beginners often struggle to understand theories expressed in complex mathematical formulas and to master computer programming. To remedy this situation, this article provides a practical, R‐based introduction to community ecology by illustrating core concepts (vital rates, carrying capacity, density dependence) and models that can be used to explore the patterns of species abundance and diversity. The structure of this article consists of three modeling exercises, each asking a general question that can be answered by a combination of theory and R programming: (1) what determines the abundance of species, and what makes a population persist and go extinct?; (2) what determines the distribution of species and species diversity?; (3) what determines the relative abundance of species and what allows species to coexist? Through the exercises, I discuss the following five concepts and ideas that provide valuable insights into the questions: (i) the tragedy of the commons, (ii) the theory of island biogeography, (iii) competitive exclusion, (iv) the neutral theory of biodiversity, and (v) frequency dependence. These materials are thus designed to guide the reader in developing an intuition for ecological thinking that will help capture the essence of the global environmental and biodiversity crisis. Although this article does not delineate the scope and depth of the vast field of community ecology, I hope that it will motivate the reader to step up to a more formal introduction to community ecology.

Evaluation Of Accuracy And Reliability Of Artificial Intelligence-Based Fully Automated And Semi-Automated Cephalometric Analysis Software In Comparison With Manual Cephalometric Analysis

Introduction: Lateral cephalometric analysis stands as a pivotal diagnostic tool within the realm of Orthodontics, often regarded as the gold standard. Over time, several computerised programs have surfaced, aiming to streamline the process of digital cephalometric analysis, thereby offering efficiency gains and time savings. The current study sought to assess the diagnostic accuracy and reliability of four computerised cephalometric analysis programs. These encompassed three digital software solutions alongside an online artificial intelligence platform. The objective was to compare the digital approaches against the traditional method of manual cephalometric tracing. Materials and methodology: This study analysed thirty pre-treatment lateral cephalometric radiographs of individuals undergoing orthodontic treatment. Four computer programs—Dolphin Imaging software, Nemoceph, and FACAD for semi-automated cephalometric analysis, and WebCeph for artificial intelligence-based cephalometric analysis—were evaluated and compared with manual cephalometric tracing. Measurements were taken for five skeletal, four dental, and one soft tissue parameters, including six angular and four linear measurements derived from Steiner, McNamara, and Tweed cephalometric analysis. Fifteen cephalometric radiographs were remeasured to assess the intra-examiner correlation coefficient. One-way ANOVA was used to compare different cephalometric points among the various tracing methods, and multiple comparisons were performed using the Post Hoc Tukey’s test. Results: No significant difference was noted in SNA, SNB, ANB, FMA, IMPA, 1 to Pt A, 1 to N-A and nasolabial angle between the software. While 1 to A Pog and 1 to N-B revealed significant differences between the software. FACAD vs. NemoCeph showed a significant difference with a mean difference of -0.30 and -0.30 and a p-value of 0.03 and 0.02 for 1 to A Pog and 1 to N-B values respectively and this variability was noted between FACAD and NemoCeph. Conclusion: Digital and AI-based software have shown accuracy comparable to manual tracing. Among the software, Dolphin Imaging proved to be the most reliable, followed by WebCeph and NemoCeph, with FACAD showing the most variability. The variability, which lacked clinical significance, was primarily due to challenges in identifying certain landmarks.

Investigation of dissolution kinetics of colemanite in propionic acid solutions

ABSTRACT Colemanite is one of the raw materials used to produce boron compounds which have a constituent of the richest composition ratio boron trioxide ( B 2 O 3 ) . The main purpose of this study is to investigate the dissolution kinetics of colemanite in a propionic acid solution. Reaction temperature, solid–liquid ratio, propionic acid concentration, stirring speed, and particle size were chosen as relevant parameters in the dissolution. According to the results of the study, the dissolution rate was directly proportional to the rise in the reaction temperature and inversely proportional to the increase in the solid–liquid ratio, acid concentration, and particle size. Additionally, it was observed that stirring speed did not significantly effect. Experiment results were correlated with the use of the Statistica10 software programme for linear regression. For the dissolution kinetics of colemanite, a model that can control the reaction was obtained by using homogeneous and heterogeneous reaction models. It was determined that the dissolution rate of the process was controlled by diffusion through the product film (ash) and the Activation energy was calculated as 37.51 kJ . mo l − 1 . Experimental data were analyzed with graphical and statistical methods, and a semi-empirical mathematical model was determined, which included the parameters of the study.

How and why to follow best practices for testing mediation models with missing data.

Mediation models are often conducted in psychology to understand mechanisms and processes of change. However, current best practices for handling missing data in mediation models are not always used by researchers. Missing data methods, such as full information maximum likelihood (FIML) and multiple imputation (MI), are best practice methods of handling missing data. However, FIML or MI are rarely used to handle missing data when testing mediation models, instead analyses used listwise deletion methods, the default in popular software. Compared to listwise deletion, the implementation of FIML or MI to handle missing data reduces parameter estimate bias, while maintaining the sample collected to maximise power and generalizability of results. In this tutorial, we review how to implement full-information maximum likelihood and MI using best practice methods of testing the indirect effect. We demonstrate how to implement these methods using both R and JASP, which are both free, open-source software programmes and provide online supplemental materials for these demonstrations. These methods are demonstrated using two example analyses, one using a cross-sectional mediation model and one using a longitudinal mediation model examining how student-athletes reported worry about COVID predicts their perceived stress, which in turn predicts satisfaction with life.

Model of time-distance curve of electromagnetic waves diffracted on a local feature in the georadar study of permafrost zone rock layers

In GPR (georadar) studies, one of the most popular procedures for determining electromagnetic waves propagation velocity in a rock mass is the selection of theoretical hyperbolic time-distance curves and subsequent comparison with the time-distance curve obtained from a GPR measurement. This procedure is based on the model of homogeneous medium, but nowadays the subject of GPR study is often inhomogeneous media, such as horizontally layered media characteristic of loose permafrost zone sediments. The paper presents the findings of studying the formation of hyperbolic time-distance curves of georadar impulses in a horizontally layered medium without taking into account the dispersion and absorption of electromagnetic waves. On the basis of geometrical optics laws, formulas were derived to calculate the shape of the hyperbolic lineup of georadar impulses reflected from a local feature in a multilayer frozen rock mass. On the example of a permafrost zone rock mass containing a layer of unfrozen rocks, the effect of the thicknesses of rock layers and their relative dielectric permittivity on the apparent dielectric permittivity resulting from the calculation of the theoretical hyperbolic time-distance curve was shown. The conditions under which it is impossible to determine the presence of a layer of unfrozen rocks from a hyperbolic time-distance curve are also presented. The established regularities were tested on synthetic georadar radargrams calculated in the gprMax software program. The findings of the theoretical studies were confirmed by the comparison with the results of the analysis of the georadar measurements computer simulation data in the gprMax system (the relative error was less than 0.5%).

VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems

We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summaryProgram Title: VAN-DAMMECPC Library link to program files::https://doi.org/10.17632/zcgw2n5bjf.1Licensing provisions: GNU General Public License 3Programming language: C++ and CUDANature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability.Solution method: Quantum control, GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.

Evaluation of the Antecedent Saturation and Rainfall Conditions on the Slope Failure Mechanism Triggered by Rainfalls

The stability analysis of rainfall-induced slope failures considers a number of factors including the characteristics of the rainfall, vegetation, geometry of the slope, unsaturated soil characteristics, infiltration capacity, and saturation degree variations. Amongst all these factors, this study aims to investigate the effects of the antecedent rainfall and saturation conditions. A numerical modeling study was conducted using finite difference code software on a representative slope geometry with two different soil types. Two scenarios were followed: The first involved the application of three different rainfall intensities for varying initial saturation levels between 40% and 60%, representing the antecedent saturation conditions. The second scenario involved modeling successive rainfalls for a typical initial saturation degree of 50%. The impact of antecedent rainfall was assessed by determining the time required for failure during the application of a main extreme rainfall after a preceding rainfall of varying durations. Consequently, a zone of susceptible time for failure was suggested for use as a criterion in hazard management, allowing for the tracking of rainfall and its duration through the proposed chart for potential failures. Once the anticipated critical rainfall intensities have been determined through a meteorological analysis, a risk assessment for a specific slope can be conducted using the proposed practical procedure. Accordingly, a control mechanism may be established to detect the potential for a natural hazard. Furthermore, the proposed procedure was applied to a case study, whose modeling insights were in harmony with the real conditions of the slope failure. Thus, this demonstrated the significance of the antecedent conditions in modeling landslides triggered by rainfalls.

СЕМАНТИЧЕСКАЯ БЛИЗОСТЬ АСТРОНОМИЧЕСКИХ ПОНЯТИЙ, ОБОЗНАЧАЮЩИХ НЕБЕСНЫЕ ТЕЛА

The problem of determining the semantic similarity of astronomical terms denoting celestial objects in a school astronomy course is considered. A distributive method is used, which consists in comparing the distributions of concepts in the astronomy textbook. With the help of special computer programs, punctuation marks and stop words are removed from the text, the number of mentions of various terms is deter-mined, the cosine measure of proximity and the semantic distance between concepts are calculated. This allows: 1) to reveal frequently used astronomical concepts denoting celestial bodies; 2) to obtain matrices of proximity and semantic distances between the concepts of "galaxy", "star", "Sun", "planet", "black hole", "white dwarf", etc.; 3) to create a mental map of the celestial bodies that make up the Solar system, taking into account the distances between the terms designating them 4) to build clouds of concepts "types of stars", "Earth – star – Universe". To model the semantic space of the textbook, the Graf.pas computer program is used, which randomly moves vertices in 2D or 3D space, each time calculating the potential energy W of the elastic rods connecting them and choosing such an arrangement of vertices that W is minimal. It is believed that the closely located vertices are connected by rigid rods, and the far removed ones are connected by elastic rubber threads.